Data Science
Wide range of expertise in data processing, mathematical modelling, regressions, machine learning and forecasting.
Introduction
Advancements in communications and computer electronics have enabled collection of vast amounts of data in a wide range of fields. Successful processing and interpretation of this data provides a considerable business advantage. This has propelled the success of Big Data and has created a big demand for data science. Data science requires a solid STEM background and good knowledge of statistics, mathematical modelling and computation techniques. This makes the members of the ConSenSo team ideal candidates for any challenge in the field.
Previous projects
- Linear regressions:
- Company fundamentals regressed against stock price movements.
- Market factors regressed against stock price movements.
- Decision trees.
- Company fundamentals regressed against stock price movements.
- Random forest.
- Company fundamentals regressed against stock price movements.
- Nowcasting.
- Trading volume and price data regressed against future price movements.
- Kalman filtering used to improve model accuracy.
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